磺酸基聚合物膜质子传导率的预测模型及其应用

Sulfonated polymers, including perfluorinated, partially fluorinated and non-fluorinated polymers are the dominant materials to fabricate Proton exchange membrane (PEM), and proton conductivity is the key performance of PEMs. Currently, insufficiently high proton conductivity of PEMs is the major bottleneck that hinders its industrial scalability, such as in PEM fuel cells. To pursue PEMs with high proton conductivity, a quantitative predictive model for proton conductivity may bring breakthrough in the deeper understanding of theories and the design of material and process. The key is to clarify the characteristic structures, the associated mechanisms for their formation, and their relationship with the proton conductivity of PEMs. We utilize molecular dynamics simulation, big data (data mining) and small angle scattering to exhaustively accumulate the characteristic structures of those PEMs either commercially available or fabricable according to the cutting-edge reports. Then a set of molecular dynamics simulations can be conducted to address the physics behind the formation of these characteristic structures. It stands on theories for microphase separation because most of the sulfonated polymers used to fabricate PEMs have typical amphiphilic block copolymers and ionomer structures. Further via the parameterization of the ingredients and the processing, together with the parameters for the structures and performance, implicit and explicit predictive models for proton conductivity can be constructed. Expand the predictive models using Bayesian analysis, theories and models about the transport of protons in PEMs can be deduced and developed. Strategies for PEMs with high proton conductivities from novel polymers with alternative structures, the selection of inorganic/organic fillers, the fabrication and post-processing are proposed and followed. Overall, this work can shed light on the development of PEMs with high proton conductivity, from theory to practice.

酸性质子交换膜主要由磺酸基聚合物（含全氟、偏氟及无氟三类）制备，而质子传导率是膜的核心性能指标。建立质子传导率的定量预测模型，有利于理论模型的深入发展和材料工艺设计的效率提升，将有力地推进高性能磺酸基聚合物质子交换膜的研发，其难点在于明确质子交换膜的特征结构、结构形成机制和结构性能关系。本项目利用分子动力学模拟、大数据和小角散射，梳理出商业化和文献报道的高性能磺酸基聚合物质子交换膜的特征结构；从两亲性嵌段高分子和离聚物微相分离的理论出发，阐明膜特征结构的形成机制；基于膜材料组成、制膜工艺、结构和性能指标构建数据库，建立质子传导性能的预测模型；应用该预测模型，发展磺酸基聚合物膜中质子传导理论，给出高性能质子交换膜的聚合物结构、材料组成、制膜与后处理工艺设计方案，并通过制备、结构性能测试验证预测模型的实用性，为开发新型高性能磺酸基聚合物质子交换膜提供指导。

围绕磺酸基聚合物膜和聚酰胺水处理两类膜材料，探索出一套具有特色的多学科交叉、理论计算模拟、表征实验和大数据研究有机结合的研究方法。利用该方法，开展了聚合物膜结构解析和传质的小角散射和计算机模拟，以及聚合物膜结构性能关系大数据两方面研究。通过将高分子物理中经典的小角散射、流变和分子动力学模拟耦合，解析了铸膜液中的微观结构和弛豫信号，进而用大数据思路与膜的力学性能建立了半定量联系，揭示了水在聚合物膜中的分布和传质机理；利用数据挖掘和机器学习的大数据研究遴选出了几种具有潜在产业前景的全氟磺酸质子交换膜复合填充材料并阐明了其背后的热力学和动力学机制；建立了针对碳氢磺酸基质子交换膜的高通量筛选以及核心性能指标包括质子传导率、机械性能、燃料渗漏率、热解温度以及膜电极的极化曲线的可靠预测模型；同时对聚砜、聚醚砜和聚偏氟乙烯材质的微滤、超滤和纳滤膜的预测模型、开发出polySML软件平台，可为高分子材料结构和性能的预测和设计提供可靠的、可实践的方案决策参考。资助期间发表含J. Mater. Chem. A, J. Membr. Sci., ACS Appl. Mater. Inter.等论文21篇，申请授权软件著作权3项，受邀做国际国内会议口头报告14次，培养博士研究生3名。